Technology

Riff

Riff is a tool that helps groups analyze and visualize multiple streams of information.

What is Riff?

Riff helps your team separate signal from noise and collaborate over live data. With Riff it is very simple to start monitoring multiple sources of information, delete the “noise” and map, tag, and collaborate on what’s important. Riff’s analytics scans your data and suggests correlations and patterns that aren’t immediately obvious. Data is imported and exported in real time to keep systems integrated.

How does Riff work?

Riff makes it easy to do collaborative analytics around streams of information. You create your workspace, set the privacy level, and invite people to work on it. You then subscribe to multiple RSS, twitter, news, SMS, and email feeds and choose the collaboration, visualization, or analytics modules you need for your work.

Automatic feature extraction, data classification and tagging

The automatic feature extraction, data classification and tagging module is an architecturally extensible module that allows the introduction of machine learning algorithms (e.g., Bayesian, SVM). These components extract and augment the features (tags or metadata) from multiple data streams, such as: source and target geo-location, time, route of transmission (e.g., person-to-person, waterborne), etc. In addition, these components help detect relationships between these extracted features within a collaborative space or across different collaborative spaces. Furthermore, with human input, these components can suggest possible events or event types (e.g., at the earliest stages of a disease outbreak: “there is an unknown respiratory event, transmitted person-to-person, detected in location X, and with a certain spatio-temporal pattern”).

Human input, hypotheses generation and review

The human input and review module is exposed as a set of functionalities that allows users to comment, tag, and semantically rank the elements (positive, neutral, or negative). Additionally, users can generate and test multiple hypotheses in parallel, further collect and rank sets of related items (evidence), and model against baseline information (for cyclical or known events). The system maintains a list of ongoing possible threats allowing domain experts to focus their field information and either confirm or reject the hypotheses created. That feedback is then fed into the system to update (increase or decrease) the reliability of the sources and the credibility of the users in light of their inferences or decisions.

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Who Is Using It?

Thomson Reuters Foundation Emergency Information Service (EIS), Haiti: Riff was used by the Thomson Reuters Foundation EIS team to receive tens of thousands of text messages via Earthquake Response Project 4636 and dispatch them to other tools and relief coordination efforts. EIS also sent more than 1 million text messages to the Haitian population with information to help them recover after the earthquake.